2023

B. Schäfl, L. Gruber, J. Brandstetter, and S. Hochreiter (2023) G-Signatures: Global Graph Propagation with Randomized Signatures. arXiv:2302.08811, 2023-02-17. (more) (download)

2022

V. T. Tran, L. Lewis, H.-Y. Huang, J. Kofler, R. Kueng, S. Hochreiter, and S. Lehner (2022) Using Shadows to Learn Ground State Properties of Quantum Hamiltonians. Machine Learning and the Physical Sciences - NeurIPS 2022, 2022-12-03. (more) (download)

S. Sanokowski, W. Berghammer, J. Kofler, S. Hochreiter, and S. Lehner (2022) One Network to Approximate Them All: Amortized Variational Inference of Ising Ground States. Machine Learning and the Physical Sciences - NeurIPS 2022, 2022-12-03. (more) (download)

S. Hochreiter (2022) Toward a broad AI. Communications of the ACM, 65, 4, 56-57, 2022-03-19. (more) (download)

2021

A. Mayr, S. Lehner, A. Mayrhofer, C. Kloss, S. Hochreiter, and J. Brandstetter (2021) Boundary Graph Neural Networks for 3D Simulations. arXiv:2106.11299, 2021-06-21. (more) (download)

A. Mayr, S. Lehner, A. Mayrhofer, C. Kloss, S. Hochreiter, and J. Brandstetter (2021) Learning 3D Granular Flow Simulations. arXiv: 2105.01636, 2021-05-04. (more) (download)

X. He, Y. Chen, and P. Ghamisi (2021) Dual Graph Convolutional Network for Hyperspectral Image Classification with Limited Training Samples. IEEE Transactions on Geoscience and Remote Sensing, 2021-03-08. (more) (download)

F. Kratzert, D. Klotz, M. Gauch, C. Klingler, G. Nearing, and S. Hochreiter (2021) Large-Scale River Network Modeling Using Graph Neural Networks. EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13375, 2021-03-03. (more) (download)

2020

H. Martin, D. Bucher, Y. Hong, R. Buffat, C. Rupprecht, and M. Raubal (2020) Graph-ResNets for short-term traffic forecasts in almost unknown cities. Proceedings of the NeurIPS 2019 Competition and Demonstration Track, PMLR 123, 153-163, 2020-08-19. (more) (download)

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